TINJAUAN LITERATUR SISTEMATIS PENDEKATAN DEEP LEARNING UNTUK DETEKSI DAN PERHITUNGAN POHON KELAPA SAWIT

Authors

  • Raden Gafur Wijayanto
  • Reza Fauzi
  • Anton Satria Prabuwono

Abstract

Kelapa sawit merupakan komoditas utama dalam industri perkebunan yang membutuhkan manajemen efektif, terutama dalam pendeteksian dan penghitungan pohon guna meningkatkan produktivitas dan efisiensi operasional. Pendekatan manual memiliki keterbatasan dalam akurasi dan efisiensi, sehingga deep learning menjadi solusi yang menjanjikan. Penelitian ini menerapkan metode Systematic Literature Review (SLR) untuk mengidentifikasi teknik terbaru dalam deteksi dan penghitungan pohon kelapa sawit menggunakan citra penginderaan jauh. Dari 15 artikel yang dianalisis (2019–2024), berbagai metode ditemukan, termasuk Multi-level Attention Domain Adaptation Network (MADAN), Multi-class Oil Palm Detection Approach (MOPAD), YOLO, CNN, ANN, dan ResNet, dengan sumber data dari Google Earth, citra satelit, serta UAV. YOLOv4 mencatat F1-Score tertinggi 97,74%, sedangkan ANN mencapai akurasi 98,29%. Pemanfaatan UAV terbukti meningkatkan akurasi deteksi dibandingkan citra satelit. Tantangan utama meliputi kebutuhan dataset berkualitas tinggi, variabilitas kondisi lingkungan, serta integrasi ke dalam sistem manajemen perkebunan. Studi ini menyimpulkan bahwa deep learning efektif dalam otomatisasi deteksi dan penghitungan pohon kelapa sawit, namun optimalisasi model dan pengembangan teknik baru masih diperlukan untuk meningkatkan akurasi serta penerapan dalam skala industri guna mendukung pertanian presisi dan keberlanjutan sektor perkebunan

References

E. Onoja, S. Chandren, F. I. Abdul Razak, N. A. Mahat, and R. A. Wahab, “Oil Palm (Elaeis guineensis) Biomass in Malaysia: The Present and Future Prospects,†Aug. 01, 2019, Springer Netherlands. doi: 10.1007/s12649-018-0258-1.

D. Khatiwada, C. Palmén, and S. Silveira, “Evaluating the palm oil demand in Indonesia: production trends, yields, and emerging issues,†Biofuels, vol. 12, no. 2, pp. 135–147, 2021, doi: 10.1080/17597269.2018.1461520.

A. Abubakar, S. Kasim, M. Y. Ishak, and M. K. Uddin, “Maximizing Oil Palm Yield: Innovative Replanting Strategies for Sustainable Productivity,†Oct. 01, 2023, Bilingual Publishing Group. doi: 10.30564/jees.v5i2.5904.

I. N. Md Rodi and S. Daliman, “Optimizing Oil Palm Tree Inventory Management with Advanced Drone-Based Image Recognition Techniques,†Malaysian Journal of Bioengineering and Technology (MJBeT), vol. 1, no. 1, pp. 66–81, Jun. 2024, doi: 10.70464/mjbet.v1i1.1274.

N. L. Rane, M. Paramesha, S. P. Choudhary, and J. Rane, “Partners Universal International Innovation Journal (PUIIJ) Artificial Intelligence, Machine Learning, and Deep Learning for Advanced Business Strategies: A Review,†2024, doi: 10.5281/zenodo.12208298.

W. Mengist, T. Soromessa, and G. Legese, “Method for conducting systematic literature review and meta-analysis for environmental science research,†MethodsX, vol. 7, p. 100777, Feb. 2020, doi: 10.1016/j.mex.2019.100777.

E. A. Felix and S. P. Lee, “Systematic literature review of preprocessing techniques for imbalanced data,†IET Software, vol. 13, no. 6, pp. 479–496, Dec. 2019, doi: 10.1049/iet-sen.2018.5193.

W. Li, R. Dong, H. Fu, and L. Yu, “Large-scale oil palm tree detection from high-resolution satellite images using two-stage convolutional neural networks,†Remote Sens (Basel), vol. 11, no. 1, Jan. 2019, doi: 10.3390/rs11010011.

J. Zheng et al., “Cross-regional oil palm tree counting and detection via a multi-level attention domain adaptation network,†ISPRS Journal of Photogrammetry and Remote Sensing, vol. 167, pp. 154–177, Sep. 2020, doi: 10.1016/j.isprsjprs.2020.07.002.

J. Zheng et al., “Growing status observation for oil palm trees using Unmanned Aerial Vehicle (UAV) images,†ISPRS Journal of Photogrammetry and Remote Sensing, vol. 173, pp. 95–121, Mar. 2021, doi: 10.1016/j.isprsjprs.2021.01.008.

J. Zheng, W. Wu, S. Yuan, H. Fu, W. Li, and L. Yu, “Multisource-Domain Generalization-Based Oil Palm Tree Detection Using Very-High-Resolution (VHR) Satellite Images,†IEEE Geoscience and Remote Sensing Letters, vol. 19, 2022, doi: 10.1109/LGRS.2021.3061726.

K. Yarak, A. Witayangkurn, K. Kritiyutanont, C. Arunplod, and R. Shibasaki, “Oil palm tree detection and health classification on highâ€resolution imagery using deep learning,†Agriculture (Switzerland), vol. 11, no. 2, pp. 1–17, Feb. 2021, doi: 10.3390/agriculture11020183.

Y. C. Putra, A. W. Wijayanto, and G. A. Chulafak, “Oil palm trees detection and counting on Microsoft Bing Maps Very High Resolution (VHR) satellite imagery and Unmanned Aerial Vehicles (UAV) data using image processing thresholding approach,†Ecol Inform, vol. 72, Dec. 2022, doi: 10.1016/j.ecoinf.2022.101878.

C. C. Lee, V. C. Koo, T. S. Lim, Y. P. Lee, and H. Abidin, “A multi-layer perceptron-based approach for early detection of BSR disease in oil palm trees using hyperspectral images,†Heliyon, vol. 8, no. 4, Apr. 2022, doi: 10.1016/j.heliyon.2022.e09252.

M. A. A. Asming, A. M. Ibrahim, and I. M. Abir, “Processing and classification of landsat and sentinel images for oil palm plantation detection,†Remote Sens Appl, vol. 26, Apr. 2022, doi: 10.1016/j.rsase.2022.100747.

H. Wibowo, I. S. Sitanggang, M. Mushthofa, and H. A. Adrianto, “Large-Scale Oil Palm Trees Detection from High-Resolution Remote Sensing Images Using Deep Learning,†Big Data and Cognitive Computing, vol. 6, no. 3, Sep. 2022, doi: 10.3390/bdcc6030089.

S. Samuel, K. R. Prilianti, H. Setiawan, and P. Mimboro, “Metode Deteksi Pokok Pohon Secara Automatis pada Citra Perkebunan Sawit Menggunakan Model Convolutional Neural Network (CNN) pada Perangkat Lunak Sistem Informasi Geografis,†Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 9, no. 7, pp. 1689–1698, Dec. 2022, doi: 10.25126/jtiik.2022976772.

Y. C. Putra and A. W. Wijayanto, “Automatic detection and counting of oil palm trees using remote sensing and object-based deep learning,†Remote Sens Appl, vol. 29, Jan. 2023, doi: 10.1016/j.rsase.2022.100914.

D. O. Pribadi et al., “Mapping smallholder plantation as a key to sustainable oil palm: A deep learning approach to high-resolution satellite imagery,†Applied Geography, vol. 153, Apr. 2023, doi: 10.1016/j.apgeog.2023.102921.

S. T. Deyosky and M. I. Taftazani, “Perhitungan Jumlah Pohon Kelapa Sawit Pada Citra Ortofoto Menggunakan Algoritma Template Matching dan Faster R-CNN,†Journal of Geospatial Science and Technology, vol. 1, no. 1, pp. 44–51, Dec. 2023, doi: 10.22146/jgst.v1i1.5423.

M. R. Rahman, R. Kusumawati, and F. Fatimah, “Deteksi Objek Menghitung Pohon Kelapa Sawit Menggunakan Metode Deep Learning,†BINA: JURNAL PEMBANGUNAN DAERAH, vol. 2, no. 1, pp. 45–51, Aug. 2023, doi: 10.62389/bina.v2i1.51.

D. Yana Armanto, S. Agustian Hudjimartsu, and E. Hermawan, “Identifikasi Perhitungan Pohon Kelapa Sawit Otomatis Dengan Menggunakan Metode Convolutional Neural Network (CNN),†JATI (Jurnal Mahasiswa Teknik Informatika), vol. 8, no. 3, pp. 2648–2654, May 2024, doi: 10.36040/jati.v8i3.9525.

D. S. Prasvita, M. M. Santoni, and N. Falih, “Deteksi Pohon Kelapa Sawit dengan Pendekatan Deep Learning pada Citra Multispectral di Indonesia,†The Indonesian Journal of Computer Science, vol. 13, no. 2, Apr. 2024, doi: 10.33022/ijcs.v13i2.3753.

K. Kipli et al., “Deep learning applications for oil palm tree detection and counting,†Oct. 01, 2023, Elsevier B.V. doi: 10.1016/j.atech.2023.100241.

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Published

2025-05-02

Issue

Vol. 30 No. 1 (2025)

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